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Record W2710077043 · doi:10.1590/0004-282x20170044

Autosomal recessive spastic ataxia of Charlevoix-Saguenay: a family report from South Brazil

2017· article· en· W2710077043 on OpenAlexaboutno aff
Daniela Burguêz, Camila Maria de Oliveira, Marcio Aloísio Bezerra Cavalcanti Rockenbach, Helena Fussiger, Leonardo Vedolin, Pablo Brea Winckler, Marcelo Krieger Maestri, Alessandro Finkelsztejn, Filippo M. Santorelli, Laura Bannach Jardim, Jonas Alex Morales Saute

Bibliographic record

VenueArquivos de Neuro-Psiquiatria · 2017
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAtaxiaSpasticityMedicineFrameshift mutationPediatricsSpasticMutationGeneticsPsychiatryPhysical medicine and rehabilitationBiologyCerebral palsy

Abstract

fetched live from OpenAlex

Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is an early-onset, neurodegenerative disorder caused by mutations in SACS, firstly reported in Quebec, Canada. The disorder is typically characterized by childhood onset ataxia, spasticity, neuropathy and retinal hypermyelination. The clinical picture of patients born outside Quebec, however, is often atypical. In the present article, the authors describe clinical and neuroradiological findings that raised the suspicion of an ARSACS diagnosis in two female cousins with Germanic background from Rio Grande do Sul, Brazil. We present a review on the neuroimaging, ophthalmologic and neurophysiologic clues for ARSACS diagnosis. The early-onset, slowly progressive, spastic-ataxia phenotype of reported patients was similar to ARSACS patients from Quebec. The SACS sequencing revealed the novel homozygous c.5150_5151insA frameshift mutation confirming the ARSACS diagnosis. ARSACS is a frequent cause of early onset ataxia/spastic-ataxia worldwide, with unknown frequency in Brazil.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.293
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2017
Admission routes1
Has abstractyes

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